Effect of Endurance on Gastrocnemius Muscle with Exercise by Employing EMG Am...ijtsrd
Muscle fatigue is a common experience in daily life. Many authors have defined it as the incapacity to maintain the required or expected force, and therefore, force, power and torque recordings have been used as direct measurements of muscle fatigue. In addition, the measurement of these variables combined with the measurement of surface electromyography sEMG recordings which can be measured during all types of movements during exercise may be useful to assess and understand muscle fatigue. EMG signal can be easily analyzed in time domain, frequency domain and time frequency domain. The time domain features are the most popular in EMG pattern recognition because they are easy and quick to calculate and they do not require a transformation. The purpose of this study was to analyze the fatigue and to study the endurance occurrence in the Gastrocnemius muscle with a pre defined exercise protocol for the targeted muscle. For this purpose, sEMG Amplitude parameters were characterized. Relation between EMG features like mean, force, standard deviation, etc. is verified for fatigue detection as well as to identify the Endurance developed in the Gastrocnemius muscle. Gaurav Patti | Poonam Kumari "Effect of Endurance on Gastrocnemius Muscle with Exercise by Employing EMG Amplitude Parameters" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-4 | Issue-5 , August 2020, URL: https://www.ijtsrd.com/papers/ijtsrd33222.pdf Paper Url :https://www.ijtsrd.com/engineering/other/33222/effect-of-endurance-on-gastrocnemius-muscle-with-exercise-by-employing-emg-amplitude-parameters/gaurav-patti
Non-uniform electromyographic activity during fatigue and recovery of the vas...Nosrat hedayatpour
The aim of the study was to investigate EMG signal features
during fatigue and recovery at three locations of the vastus
medialis and lateralis muscles.
Correlation Analysis of Electromyogram SignalsIJMTST Journal
An inability to adapt myoelectric interfaces to a user’s unique style of hand motion. The system also adapts
the motion style of an opposite limb. These are the important factors inhibiting the practical application of
myoelectric interfaces. This is mainly attributed to the individual differences in the exhibited electromyogram
(EMG) signals generated by the muscles of different limbs. In this project myoelectric interface easily adapts
the signal from the users and maintains good movement recognition performance. At the initial stage the
myoelectric signal is extracted from the user by using the data acquisition system. A new set of features
describing the movements of user’s is extracted and the user’s features are classifed using SVM
classification. The given signal is then compared with the database signal with the accuracy of 90.910 %
across all the EMG signals.
Review of Software to Analyse the Physical Conditions of the Athletes using sEMGijtsrd
Electromyography measures muscle responses of a nerve's simulation of the muscle. EMG is generally measured or recorded through surface, needle or wired electrodes. The surface electromyography is a commonly used technique for measuring the muscle exhilaration. The purpose of this project is to evaluate the use of sEMG in the practical context and to translate the given context to the appropriate analysis. The sEMG are used on the athletes while they are running and respective results are being noted. By using this technique our project wishes to implement an android iOS application to calculate the corresponding values which are being noted by the particular device which we have been made. The signals which are being given by the device is converted into the appropriate percentage values or graphs which can be determined into giving a complete overview about the person whom he is checking and can suggest the diets and exercises to make that person fit to the expectations. This software is mainly look forward for the development of the future athletes which can win the prizes. This platform provides immense forms of diets which are based on the values or results which have been depicted. Performance analysis in sports is considered to be an integral component of understanding the requirements of the optimal performance. Several measurement techniques have been used to inspect the performance of the best athletes today. it is mostly commonly done in laboratory where physiology and bio mechanics can be analyzed. in this system first, the coaches conduct a study about the agility, strength and nutrition of the excellent players of the country. Then the coaches of the respective clubs or the schools check each and every student's physical condition and compare with the stored data in order to train them. The project has got direct advantage to the aspiring future athletes of the country and also to the health conscious society by providing them a device to calculate on their body metrics and work around to improve on it. Tenwin James K | Varun Vincent | Lino Louis | Vishnuraj T | Aneesh Chandran "Review of Software to Analyse the Physical Conditions of the Athletes using sEMG" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-4 | Issue-4 , June 2020, URL: https://www.ijtsrd.com/papers/ijtsrd31546.pdf Paper Url :https://www.ijtsrd.com/computer-science/embedded-system/31546/review-of-software-to-analyse-the-physical-conditions-of-the-athletes-using-semg/tenwin-james-k
Electromyography Analysis for Person IdentificationCSCJournals
Physiological descriptions of the electromyography signal and other literature say that when we make a motion, the motor neurons of respective muscle get activated and all the innervated motor units in that zone produce motor unit action potential. These motor unit action potentials travel through the muscle fibers with conduction velocity and superimposed signal gets recorded at the electrode site. Here we have taken an analogy from the speech production system model as the excitation signal travels through vocal tract to produce speech; similarly, an impulse train of firing rate frequency goes through the system with impulse response of motor unit action potentials and travels along the muscle fiber of that person. As the vocal tract contains the speaker information, we can also separate the muscle fiber pattern part and motor unit discharge pattern through proper selection of features and its classification to identify the respective person. Cepstral and non uniform filter bank features models the variation in the spectrum of the signals. Vector quantization and Gaussian mixture model are the two techniques of pattern matching have been applied.
Effect of Endurance on Gastrocnemius Muscle with Exercise by Employing EMG Am...ijtsrd
Muscle fatigue is a common experience in daily life. Many authors have defined it as the incapacity to maintain the required or expected force, and therefore, force, power and torque recordings have been used as direct measurements of muscle fatigue. In addition, the measurement of these variables combined with the measurement of surface electromyography sEMG recordings which can be measured during all types of movements during exercise may be useful to assess and understand muscle fatigue. EMG signal can be easily analyzed in time domain, frequency domain and time frequency domain. The time domain features are the most popular in EMG pattern recognition because they are easy and quick to calculate and they do not require a transformation. The purpose of this study was to analyze the fatigue and to study the endurance occurrence in the Gastrocnemius muscle with a pre defined exercise protocol for the targeted muscle. For this purpose, sEMG Amplitude parameters were characterized. Relation between EMG features like mean, force, standard deviation, etc. is verified for fatigue detection as well as to identify the Endurance developed in the Gastrocnemius muscle. Gaurav Patti | Poonam Kumari "Effect of Endurance on Gastrocnemius Muscle with Exercise by Employing EMG Amplitude Parameters" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-4 | Issue-5 , August 2020, URL: https://www.ijtsrd.com/papers/ijtsrd33222.pdf Paper Url :https://www.ijtsrd.com/engineering/other/33222/effect-of-endurance-on-gastrocnemius-muscle-with-exercise-by-employing-emg-amplitude-parameters/gaurav-patti
Non-uniform electromyographic activity during fatigue and recovery of the vas...Nosrat hedayatpour
The aim of the study was to investigate EMG signal features
during fatigue and recovery at three locations of the vastus
medialis and lateralis muscles.
Correlation Analysis of Electromyogram SignalsIJMTST Journal
An inability to adapt myoelectric interfaces to a user’s unique style of hand motion. The system also adapts
the motion style of an opposite limb. These are the important factors inhibiting the practical application of
myoelectric interfaces. This is mainly attributed to the individual differences in the exhibited electromyogram
(EMG) signals generated by the muscles of different limbs. In this project myoelectric interface easily adapts
the signal from the users and maintains good movement recognition performance. At the initial stage the
myoelectric signal is extracted from the user by using the data acquisition system. A new set of features
describing the movements of user’s is extracted and the user’s features are classifed using SVM
classification. The given signal is then compared with the database signal with the accuracy of 90.910 %
across all the EMG signals.
Review of Software to Analyse the Physical Conditions of the Athletes using sEMGijtsrd
Electromyography measures muscle responses of a nerve's simulation of the muscle. EMG is generally measured or recorded through surface, needle or wired electrodes. The surface electromyography is a commonly used technique for measuring the muscle exhilaration. The purpose of this project is to evaluate the use of sEMG in the practical context and to translate the given context to the appropriate analysis. The sEMG are used on the athletes while they are running and respective results are being noted. By using this technique our project wishes to implement an android iOS application to calculate the corresponding values which are being noted by the particular device which we have been made. The signals which are being given by the device is converted into the appropriate percentage values or graphs which can be determined into giving a complete overview about the person whom he is checking and can suggest the diets and exercises to make that person fit to the expectations. This software is mainly look forward for the development of the future athletes which can win the prizes. This platform provides immense forms of diets which are based on the values or results which have been depicted. Performance analysis in sports is considered to be an integral component of understanding the requirements of the optimal performance. Several measurement techniques have been used to inspect the performance of the best athletes today. it is mostly commonly done in laboratory where physiology and bio mechanics can be analyzed. in this system first, the coaches conduct a study about the agility, strength and nutrition of the excellent players of the country. Then the coaches of the respective clubs or the schools check each and every student's physical condition and compare with the stored data in order to train them. The project has got direct advantage to the aspiring future athletes of the country and also to the health conscious society by providing them a device to calculate on their body metrics and work around to improve on it. Tenwin James K | Varun Vincent | Lino Louis | Vishnuraj T | Aneesh Chandran "Review of Software to Analyse the Physical Conditions of the Athletes using sEMG" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-4 | Issue-4 , June 2020, URL: https://www.ijtsrd.com/papers/ijtsrd31546.pdf Paper Url :https://www.ijtsrd.com/computer-science/embedded-system/31546/review-of-software-to-analyse-the-physical-conditions-of-the-athletes-using-semg/tenwin-james-k
Electromyography Analysis for Person IdentificationCSCJournals
Physiological descriptions of the electromyography signal and other literature say that when we make a motion, the motor neurons of respective muscle get activated and all the innervated motor units in that zone produce motor unit action potential. These motor unit action potentials travel through the muscle fibers with conduction velocity and superimposed signal gets recorded at the electrode site. Here we have taken an analogy from the speech production system model as the excitation signal travels through vocal tract to produce speech; similarly, an impulse train of firing rate frequency goes through the system with impulse response of motor unit action potentials and travels along the muscle fiber of that person. As the vocal tract contains the speaker information, we can also separate the muscle fiber pattern part and motor unit discharge pattern through proper selection of features and its classification to identify the respective person. Cepstral and non uniform filter bank features models the variation in the spectrum of the signals. Vector quantization and Gaussian mixture model are the two techniques of pattern matching have been applied.
Embedded system for upper-limb exoskeleton based on electromyography controlTELKOMNIKA JOURNAL
A major problem in an exoskeleton based on electromyography (EMG) control with pattern recognition-based is the need for more time to train and to calibrate the system in order able to adapt for different subjects and variable. Unfortunately, the implementation of the joint prediction on an embedded system for the exoskeleton based on the EMG control with non-pattern recognition-based is very rare. Therefore, this study presents an implementation of elbow-joint angle prediction on an embedded system to control an upper limb exoskeleton based on the EMG signal. The architecture of the system consisted of a bio-amplifier, an embedded ARMSTM32F429 microcontroller, and an exoskeleton unit driven by a servo motor. The elbow joint angle was predicted based on the EMG signal that is generated from biceps. The predicted angle was obtained by extracting the EMG signal using a zero-crossing feature and filtering the EMG feature using a Butterworth low pass filter. This study found that the range of root mean square error and correlation coefficients are 8°-16° and 0.94-0.99, respectively which suggest that the predicted angle is close to the desired angle and there is a high relationship between the predicted angle and the desired angle.
Hand motion pattern recognition analysis of forearm muscle using MMG signalsjournalBEEI
Surface Mechanomyography (MMG) is the recording of mechanical activity of muscle tissue. MMG measures the mechanical signal (vibration of muscle) that generated from the muscles during contraction or relaxation action. It is widely used in various fields such as medical diagnosis, rehabilitation purpose and engineering applications. The main purpose of this research is to identify the hand gesture movement via VMG sensor (TSD250A) and classify them using Linear Discriminant Analysis (LDA). There are four channels MMG signal placed into adjacent muscles which PL-FCU and ED-ECU. The features used to feed the classifier to determine accuracy are mean absolute value, standard deviation, variance and root mean square. Most of subjects gave similar range of MMG signal of extraction values because of the adjacent muscle. The average accuracy of LDA is approximately 87.50% for the eight subjects. The finding of the result shows, MMG signal of adjacent muscle can affect the classification accuracy of the classifier.
A comparative study of wavelet families for electromyography signal classific...journalBEEI
Automatic detection of neuromuscular disorders performed using electromyography (EMG) has become an interesting domain for many researchers. In this paper, we present an approach to evaluate and classify the non-stationary EMG signals based on discrete wavelet transform (DWT). Most often researches did not consider the effect of DWT factors on the performance of EMG signals classification. This problem is still an interesting unsolved challenge. However, the selection of appropriate mother wavelet and related level decomposition is an essential issue that should be addressed in DWT-based EMG signals classification. The proposed method consists of decomposing a raw EMG signal into different sub-bands. Several statistical features were extracted from each sub-band and six wavelet families were investigated. The feature vector was used as inputs to support vector machine (SVM) classifier for the diagnosis of neuromuscular disorders. The obtained results achieve satisfactory performances with optimal DWT factors using 10-fold cross-validation. From the classification performances, it was found that sym14 is the most suitable mother wavelet at the 8th optimal wavelet level of decomposition. These simulation results demonstrated that the proposed method is very reliable for reducing cost computational time of automated neuromuscular disorders system and removing the redundancy information.
A Detail Study of Wavelet Families for EMG Pattern Recognition IJECEIAES
Wavelet transform (WT) has recently drawn the attention of the researchers due to its potential in electromyography (EMG) recognition system. However, the optimal mother wavelet selection remains a challenge to the application of WT in EMG signal processing. This paper presents a detail study for different mother wavelet function in discrete wavelet transform (DWT) and continuous wavelet transform (CWT). Additionally, the performance of different mother wavelet in DWT and CWT at different decomposition level and scale are also investigated. The mean absolute value (MAV) and wavelength (WL) features are extracted from each CWT and reconstructed DWT wavelet coefficient. A popular machine learning method, support vector machine (SVM) is employed to classify the different types of hand movements. The results showed that the most suitable mother wavelet in CWT are Mexican hat and Symlet 6 at scale 16 and 32, respectively. On the other hand, Symlet 4 and Daubechies 4 at the second decomposition level are found to be the optimal wavelet in DWT. From the analysis, we deduced that Symlet 4 at the second decomposition level in DWT is the most suitable mother wavelet for accurate classification of EMG signals of different hand movements.
The development of wireless body area sensor network (WBASN) is offer many promising
new application in the area of remote health monitoring. This paper presents a system consisting
of a force measuring device for estimation of the force ability of human muscle groups which
means (Arm Strength). It comprises at least one (pressing element) strength sensor which works
together with a force measuring microcontroller based electronic unit. This unit can accurately
measure the force exerted onto strength sensor placed inside the force measuring unit. According
to how the equipment is assorted muscle strength of different muscle group can be measured.
The measured value are converted to digital form and stored in memory.
Implementation of Radon Transformation for Electrical Impedance Tomography (E...ijistjournal
Radon Transformation is generally used to construct optical image (like CT image) from the projection data in biomedical imaging. In this paper, the concept of Radon Transformation is implemented to reconstruct Electrical Impedance Topographic Image (conductivity or resistivity distribution) of a circular subject. A parallel resistance model of a subject is proposed for Electrical Impedance Topography(EIT) or Magnetic Induction Tomography(MIT). A circular subject with embedded circular objects is segmented into equal width slices from different angles. For each angle, Conductance and Conductivity of each slice is calculated and stored in an array. A back projection method is used to generate a two-dimensional image from one-dimensional projections. As a back projection method, Inverse Radon Transformation is applied on the calculated conductance and conductivity to reconstruct two dimensional images. These images are compared to the target image. In the time of image reconstruction, different filters are used and these images are compared with each other and target image.
Time Frequency Feature Extraction Scheme based on MUAP for classification of ...rahulmonikasharma
The features of motor unit action potentials(MUAPs) are extracted from electromyographic (EMG) signals which provide information for diagnosis of neuromuscular disorders. Neuromuscular Disorders are classified into two categories Myopathic and Amyotrophic Lateral Sclerosis(ALS). ALS is a progressive neurodegenerative disease that affects nerve cells in the brain and the spinal cord. The progressive degeneration of the motor neurons in ALS eventually leads to their demise. When the motor neurons die, the ability of the brain to initiate and control muscle movement is lost hence the EMG signals of the patient of this disease are characterized by signals that have a increased value of amplitude , thereby increasing the peak to peak value of the signal. On the other hand Myopathies are a group of disorders characterized by a primary structural or functional impairment of skeletal muscle. They usually affect muscle without involving the nervous system, resulting in muscular weakness hence the EMG signals of the patients of this group of disorder are characterized by signals of shorter duration and smaller amplitude. The aim of this study, is to design a automated system which can classify the signals as ALS , Myopathic and Normal.The proposed scheme employs extracting both time and time–frequency features of a MUAP and then providing it to classifier which can classify the signals as ALS, myopathic and normal.In the proposed system, three classifiers are implemented and their results are evaluated out of which Random Forest classification technique provides the highest accuracy of 97.85%.
Central Park Physical Medicine - ElectromyographySamuel Theagene
As the medical director of Central Park Physical Medicine, PC, Dr. Samuel M. Theagene supervises pain management and patient care at the clinic, which the latest in medical technology, such as electromyography and fluoroscopy. Samuel M. Theagene, MD, has been a practicing pain specialist for over 20 years and is a board certified Interventional Pain Physician.
Measuring hand strength and manual muscle testing helps identify deficits after local injury as well as providing information on a patient’s overall strength and health. The best way to measure hand strength or manual muscle testing is with the use of a handheld dynamometer.
Embedded system for upper-limb exoskeleton based on electromyography controlTELKOMNIKA JOURNAL
A major problem in an exoskeleton based on electromyography (EMG) control with pattern recognition-based is the need for more time to train and to calibrate the system in order able to adapt for different subjects and variable. Unfortunately, the implementation of the joint prediction on an embedded system for the exoskeleton based on the EMG control with non-pattern recognition-based is very rare. Therefore, this study presents an implementation of elbow-joint angle prediction on an embedded system to control an upper limb exoskeleton based on the EMG signal. The architecture of the system consisted of a bio-amplifier, an embedded ARMSTM32F429 microcontroller, and an exoskeleton unit driven by a servo motor. The elbow joint angle was predicted based on the EMG signal that is generated from biceps. The predicted angle was obtained by extracting the EMG signal using a zero-crossing feature and filtering the EMG feature using a Butterworth low pass filter. This study found that the range of root mean square error and correlation coefficients are 8°-16° and 0.94-0.99, respectively which suggest that the predicted angle is close to the desired angle and there is a high relationship between the predicted angle and the desired angle.
Hand motion pattern recognition analysis of forearm muscle using MMG signalsjournalBEEI
Surface Mechanomyography (MMG) is the recording of mechanical activity of muscle tissue. MMG measures the mechanical signal (vibration of muscle) that generated from the muscles during contraction or relaxation action. It is widely used in various fields such as medical diagnosis, rehabilitation purpose and engineering applications. The main purpose of this research is to identify the hand gesture movement via VMG sensor (TSD250A) and classify them using Linear Discriminant Analysis (LDA). There are four channels MMG signal placed into adjacent muscles which PL-FCU and ED-ECU. The features used to feed the classifier to determine accuracy are mean absolute value, standard deviation, variance and root mean square. Most of subjects gave similar range of MMG signal of extraction values because of the adjacent muscle. The average accuracy of LDA is approximately 87.50% for the eight subjects. The finding of the result shows, MMG signal of adjacent muscle can affect the classification accuracy of the classifier.
A comparative study of wavelet families for electromyography signal classific...journalBEEI
Automatic detection of neuromuscular disorders performed using electromyography (EMG) has become an interesting domain for many researchers. In this paper, we present an approach to evaluate and classify the non-stationary EMG signals based on discrete wavelet transform (DWT). Most often researches did not consider the effect of DWT factors on the performance of EMG signals classification. This problem is still an interesting unsolved challenge. However, the selection of appropriate mother wavelet and related level decomposition is an essential issue that should be addressed in DWT-based EMG signals classification. The proposed method consists of decomposing a raw EMG signal into different sub-bands. Several statistical features were extracted from each sub-band and six wavelet families were investigated. The feature vector was used as inputs to support vector machine (SVM) classifier for the diagnosis of neuromuscular disorders. The obtained results achieve satisfactory performances with optimal DWT factors using 10-fold cross-validation. From the classification performances, it was found that sym14 is the most suitable mother wavelet at the 8th optimal wavelet level of decomposition. These simulation results demonstrated that the proposed method is very reliable for reducing cost computational time of automated neuromuscular disorders system and removing the redundancy information.
A Detail Study of Wavelet Families for EMG Pattern Recognition IJECEIAES
Wavelet transform (WT) has recently drawn the attention of the researchers due to its potential in electromyography (EMG) recognition system. However, the optimal mother wavelet selection remains a challenge to the application of WT in EMG signal processing. This paper presents a detail study for different mother wavelet function in discrete wavelet transform (DWT) and continuous wavelet transform (CWT). Additionally, the performance of different mother wavelet in DWT and CWT at different decomposition level and scale are also investigated. The mean absolute value (MAV) and wavelength (WL) features are extracted from each CWT and reconstructed DWT wavelet coefficient. A popular machine learning method, support vector machine (SVM) is employed to classify the different types of hand movements. The results showed that the most suitable mother wavelet in CWT are Mexican hat and Symlet 6 at scale 16 and 32, respectively. On the other hand, Symlet 4 and Daubechies 4 at the second decomposition level are found to be the optimal wavelet in DWT. From the analysis, we deduced that Symlet 4 at the second decomposition level in DWT is the most suitable mother wavelet for accurate classification of EMG signals of different hand movements.
The development of wireless body area sensor network (WBASN) is offer many promising
new application in the area of remote health monitoring. This paper presents a system consisting
of a force measuring device for estimation of the force ability of human muscle groups which
means (Arm Strength). It comprises at least one (pressing element) strength sensor which works
together with a force measuring microcontroller based electronic unit. This unit can accurately
measure the force exerted onto strength sensor placed inside the force measuring unit. According
to how the equipment is assorted muscle strength of different muscle group can be measured.
The measured value are converted to digital form and stored in memory.
Implementation of Radon Transformation for Electrical Impedance Tomography (E...ijistjournal
Radon Transformation is generally used to construct optical image (like CT image) from the projection data in biomedical imaging. In this paper, the concept of Radon Transformation is implemented to reconstruct Electrical Impedance Topographic Image (conductivity or resistivity distribution) of a circular subject. A parallel resistance model of a subject is proposed for Electrical Impedance Topography(EIT) or Magnetic Induction Tomography(MIT). A circular subject with embedded circular objects is segmented into equal width slices from different angles. For each angle, Conductance and Conductivity of each slice is calculated and stored in an array. A back projection method is used to generate a two-dimensional image from one-dimensional projections. As a back projection method, Inverse Radon Transformation is applied on the calculated conductance and conductivity to reconstruct two dimensional images. These images are compared to the target image. In the time of image reconstruction, different filters are used and these images are compared with each other and target image.
Time Frequency Feature Extraction Scheme based on MUAP for classification of ...rahulmonikasharma
The features of motor unit action potentials(MUAPs) are extracted from electromyographic (EMG) signals which provide information for diagnosis of neuromuscular disorders. Neuromuscular Disorders are classified into two categories Myopathic and Amyotrophic Lateral Sclerosis(ALS). ALS is a progressive neurodegenerative disease that affects nerve cells in the brain and the spinal cord. The progressive degeneration of the motor neurons in ALS eventually leads to their demise. When the motor neurons die, the ability of the brain to initiate and control muscle movement is lost hence the EMG signals of the patient of this disease are characterized by signals that have a increased value of amplitude , thereby increasing the peak to peak value of the signal. On the other hand Myopathies are a group of disorders characterized by a primary structural or functional impairment of skeletal muscle. They usually affect muscle without involving the nervous system, resulting in muscular weakness hence the EMG signals of the patients of this group of disorder are characterized by signals of shorter duration and smaller amplitude. The aim of this study, is to design a automated system which can classify the signals as ALS , Myopathic and Normal.The proposed scheme employs extracting both time and time–frequency features of a MUAP and then providing it to classifier which can classify the signals as ALS, myopathic and normal.In the proposed system, three classifiers are implemented and their results are evaluated out of which Random Forest classification technique provides the highest accuracy of 97.85%.
Central Park Physical Medicine - ElectromyographySamuel Theagene
As the medical director of Central Park Physical Medicine, PC, Dr. Samuel M. Theagene supervises pain management and patient care at the clinic, which the latest in medical technology, such as electromyography and fluoroscopy. Samuel M. Theagene, MD, has been a practicing pain specialist for over 20 years and is a board certified Interventional Pain Physician.
Measuring hand strength and manual muscle testing helps identify deficits after local injury as well as providing information on a patient’s overall strength and health. The best way to measure hand strength or manual muscle testing is with the use of a handheld dynamometer.
Kinetic study of free and immobilized protease from Aspergillus sp.IOSR Journals
In the present investigation partially purified alkaline protease from Aspergillus sp. As#6 and As#7 strains were entrapped in calcium alginate beads and characterized using casein as a substrate. Temperature and pH maxima of protease from As#6 strain showed no changes before and after immobilization and remained stable at 450C and pH 9, respectively. However km value was slightly shifted from 4.5mg/ml to 5 mg/ml. Proteases from As#7 strain showed shifting in pH optima to a more alkaline range (10.0) as compared with free enzyme (9.0). Optimum temperature for protease from As#7 strain showed changes after immobilization and shifted from 650C to 850C. However there was no significant effect on Km value but Vmax of immobilized protease from As#7 strain was also shifted from 200U/ml to 370U/ml. Immobilized protease from As#6 strain was reused for 3 cycles with 22% loss in its activity whereas immobilize protease from As#7 strain was reused for 3 cycles with 17% loss in its activity. Protease from As#7 strain has a higher affinity for the substrate and higher proteolysis activity than protease from As#6 strain. The present work concludes that Aspergillus As#7 strain may be a good source of industrial protease
Measurement of Efficiency Level in Nigerian Seaport after Reform Policy Imple...IOSR Journals
This paper focuses on the impact of reforms on port performance using Onne and Rivers ports as a reference point. It analyses the pre and post reform eras of the ports in terms of their performance. The reforms took effect from 1996 after the Federal Government of Nigeria concessioned the ports to private investors. Parameters such as Ship traffic, Cargo throughput, Ship turn round time, Berth Occupancy and personnel were used as variables for the assessment. Secondary Data were collected from the Nigerian Ports Authority and Integrated Logistic Services Nigeria (Intels) for the period 2001 to 2010 and analyzed using Data Envelopment Analysis to assess the efficiency of the port. Analysis revealed a continuous improvement in the overall efficiency of both Ports Since 2006 when the new measure was introduced. Average Ship turn-around time improved in the ports due to modern and fast cargo handling equipment and more cargo handling space which were provided. There is an increase in Ship traffic calling at the ports, resulting in increased cargo throughput and berth occupancy rate at ports of Onne and Rivers. The reform also led to more private investment in the ports’ existing and new facilities and the introduction of a World Class service in port operation. This study concludes that the Ports of Onne and Rivers are performing better under the reform programme of the Federal Government of Nigeria. It finally recommends the urgent need for a regulator to appraise the performance of the reform programme from time to time as provided by the agreement and for the full adoption and utilization of management information system (MIS) to aid performance efficiency.
“Prevalent Clinical Entities Of Hilly Regions, Aetio-Pathogenesis Factors, An...IOSR Journals
Certain Surgical Clinical Conditions Manifests To Variable Extents With High
Prevalence In Hilly Regions. The Discrete Analysis Of Different Aetio-Pathogenesis Factors & Resultant Patho-
Physiological Changes Exhibit Comparative Co-Relation To Clinical Manifestations, ManageMent GuideLines &
OverAll Result OutCome ParaMetres.
NOx Reduction of Diesel Engine with Madhuca Indica biodiesel using Selective ...IOSR Journals
A comparison analysis for different flow rates of urea-water selective catalytic reduction (SCR) has
been carried out on a direct injection diesel engine. An optimum nozzle opening pressure of 250 bar and static
injection timing of 20°bTDC is considered because these conditions only were found to give minimum emissions
and better performance. An engine set up with SCR is made to study the influence of SCR on reduction of
emissions from the diesel engine. The volume flow rate of 1, 2, 3, 4 and 5 ml/minute have been used with a
mixture of 30% urea and 70% water as SCR for the entire experiment. From the test results, it could be noted
that, among all flow rates, the volume flow rate of3 ml/minute gives better performance, combustion and lowest
emissions. Among the blends, B100 gives lowest emissions of smoke density and hydrocarbon as compared to without SCR. But in the presence of SCR, there is a drastic reduction in NOx of 17.81% for B100 as compared
to without SCR at full load condition of the engine
Effect of Fly Ash Particles on the Mechanical Properties of Zn-22%Al Alloy vi...IOSR Journals
In the present investigation, a Zn-22%Al alloy is used as the matrix material and fly ash as the filler material. The composite is produced using powder metallurgy techniques. The fly ash is added in 2%, 4%, and 6% by wt% to the sintering metal. The composite is tested for hardness, density and tensile strength test. Microstructure examination is done using a high resolution optical microscope to obtain the distribution of fly ash in the Zn-Al matrix. Test results indicate that as fly ash content is increased, there is a considerable increase in hardness and tensile strength but decrease in density.
Role of Educational Qualification of Consumers on Need Recognition: A Study w...IOSR Journals
Demographic variables are the most popular bases for segmenting the customer groups. One reason is that consumer needs, wants, preferences and usage rates often highly associated with demographic variables. Another is that demographic variables are easier to measure than the most of other type variables. Marketers are keenly interested in the size and growth rate of population in different cities, regions, nations; age distribution; educational levels; household patterns; and regional characteristics and movements. Because, on the basis of these measures only, marketers have to formulate their marketing strategies in order to fulfil the needs, wants and preferences of consumers. Moreover, demographic variables make known the ongoing trends, such as shifts in age, sex and income distribution that signal new business opportunities to the marketers. Demographic trends are highly reliable for the short and intermediate run. This paper, with a strong backing of literature, explains the role of educational qualification of consumers on recognizing a need for car.
This paper will review the works on Surface Electromyography (SEMG) signal acquisition and controlling as well as the uses of SEMG signals analysis for Transfemoral amputee's people. In the beginning, this paper will briefly go through the basic theory of myoelectric signal generation. Next, the signal acquisition & filtering techniques applied for SEMG signal will be explained. Then after this EMG signal control or actuate the myoelectric leg who was suffering from Transfemoral amputee using microcontroller. This paper gives the better controlling SEMG signal and also very smooth and easy controlling of the Prosthetic leg motor using Myoelectric Controller.
International Journal of Computational Engineering Research (IJCER) is dedicated to protecting personal information and will make every reasonable effort to handle collected information appropriately. All information collected, as well as related requests, will be handled as carefully and efficiently as possible in accordance with IJCER standards for integrity and objectivity.
A robotic arm is a Programmable mechanical arm which copies the functions of the human arm. They
are widely used in industries. Human robot-controlled interfaces mainly focus on providing rehabilitation to
amputees in order to overcome their amputation or disability leading them to live a normal life. The major
objective of this project is to develop a movable robotic arm controlled by EMG signals from the muscles of the
upper limb. In this system, our main aim is on providing a low 2-dimensional input derived from emg to move the
arm. This project involves creating a prosthesis system that allows signals recorded directly from the human body.
The arm is mainly divided into 2 parts, control part and moving part. Movable part contains the servo motor
which is connected to the Arduino Uno board, and it helps in developing a motion in accordance with the EMG
signals acquired from the body. The control part is the part that is controlled by the operation according to the
movement of the amputee. Mainly the initiation of the movement for the threshold fixed in the coding. The major
aim of the project is to provide an affordable and easily operable device that helps even the poor sections of the
amputated society to lead a happier and normal life by mimicking the functions of the human arm in terms of both
the physical, structural as well as functional aspects.
Application of EMG and Force Signals of Elbow Joint on Robot-assisted Arm Tra...TELKOMNIKA JOURNAL
Flexion-extension based on the system's robotic arm has the potential to increase the patient's elbow joint movement. The force sensor and electromyography signals can support the biomechanical system to detect electrical signals generated by the muscles of the biological. The purpose of this study is to implement the design of force sensor and EMG signals application on the elbow flexion motion of the upper arm. In this experiments, the movements of flexion at an angle of 45º, 90º and 135º is applied to identify the relationship between the amplitude of the EMG and force signals on every angle. The contribution of this research is for supporting the development of the Robot-Assisted Arm Training. The correlation between the force signal and the EMG signal from the subject studied in the elbow joint motion tests. The application of sensors tested by an experimental on healthy subjects to simulating arm movement. The experimental results show the relationship between the amplitude of the EMG and force signals on flexion angle of the joint mechanism for monitoring the angular displacement of the robotic arm. Further developments in the design of force sensor and EMG signals are potentially for open the way for the next researches based on the physiological condition of each patient.
Current motorized limb prostheses provide rudimentary functionality for the application in everyday life. Together with a
poor cosmetic appearance, this is the reason why a large percentage of amputees do not use their prosthetic device regularly. This
paper seeks to present an overview of current state of the art research on neural interfaces. The focus lies on non-invasive
recording with EMG and especially High-Density EMG sensors. Additionally, direct machine learning and pattern recognition
algorithms for the decoding of the recorded signals are discussed. Finally, promising research directions for advanced prosthesis
control will be discussed. The bionic arm uses EMG signals to control each action of the hand. In order to control them, we need to
record the EMG signal for different actions. And compare it with real-time values to move the hand in a different manner. There
are separate servo motors to control the actions of each finger separately. So these are programmed by using microcontrollers.
Expert System Analysis of Electromyogramidescitation
Electromyogram (EMG) is the record of the electrical excitation of the skeletal
muscles which is initiated and regulated by the central and peripheral nervous system.
EMGs have non-stationary properties. EMG signals of isometric contraction for two
different abnormalities namely ALS (Amyotrophic Lateral Sclerosis) which is coming under
Neuropathy and Myopathy. Neuropathy relates to the degeneration of neural impulse
whereas myopathy relates to the degeneration of muscle fibers. There are two issues in the
classification of EMG signals. In EMG’s diseases recognition, the first and the most
important step is feature extraction. In this paper, six non-linear features have been used to
classify using Support Vector Machine. In this paper, after feature extraction, feature
matrix is normalized in order to have features in a same range. Simply, linear SVM
classifier was trained by the train-train data and then used for classifying the train-test
data. From the experimental results, Lyapunov exponent and Hurst exponent is the best
feature with higher accuracy comparing with the other features, whereas features like
Capacity Dimension, Correlation Function, Correlation Dimension, Probability Distribution
& Correlation Matrix are useful augmenting features.
Bio-medical (EMG) Signal Analysis and Feature Extraction Using Wavelet TransformIJERA Editor
In this paper, the multi-channel electromyogram acquisition system is being developed using programmable
system on chip (PSOC) microcontroller to obtain the surface of EMG signal. The two pairs of single-channel
surface electrodes are utilized to measure the EMG signal obtained from forearm muscles. Then different levels
of Wavelet family are used to analyze the EMG signal. Later features in terms of root mean square, logarithm of
root mean square, centroid of frequency, as well as standard deviation were used to extract the EMG signal. The
proposed method of feature extraction for extracting EMG signal states that root means square feature extraction
method gives better performance as compared to the other features. In the near future, this method can be used to
control a mechanical arm as well as robotic arm in field of real-time processing.
"Impact of front-end architecture on development cost", Viktor TurskyiFwdays
I have heard many times that architecture is not important for the front-end. Also, many times I have seen how developers implement features on the front-end just following the standard rules for a framework and think that this is enough to successfully launch the project, and then the project fails. How to prevent this and what approach to choose? I have launched dozens of complex projects and during the talk we will analyze which approaches have worked for me and which have not.
Neuro-symbolic is not enough, we need neuro-*semantic*Frank van Harmelen
Neuro-symbolic (NeSy) AI is on the rise. However, simply machine learning on just any symbolic structure is not sufficient to really harvest the gains of NeSy. These will only be gained when the symbolic structures have an actual semantics. I give an operational definition of semantics as “predictable inference”.
All of this illustrated with link prediction over knowledge graphs, but the argument is general.
Epistemic Interaction - tuning interfaces to provide information for AI supportAlan Dix
Paper presented at SYNERGY workshop at AVI 2024, Genoa, Italy. 3rd June 2024
https://alandix.com/academic/papers/synergy2024-epistemic/
As machine learning integrates deeper into human-computer interactions, the concept of epistemic interaction emerges, aiming to refine these interactions to enhance system adaptability. This approach encourages minor, intentional adjustments in user behaviour to enrich the data available for system learning. This paper introduces epistemic interaction within the context of human-system communication, illustrating how deliberate interaction design can improve system understanding and adaptation. Through concrete examples, we demonstrate the potential of epistemic interaction to significantly advance human-computer interaction by leveraging intuitive human communication strategies to inform system design and functionality, offering a novel pathway for enriching user-system engagements.
Kubernetes & AI - Beauty and the Beast !?! @KCD Istanbul 2024Tobias Schneck
As AI technology is pushing into IT I was wondering myself, as an “infrastructure container kubernetes guy”, how get this fancy AI technology get managed from an infrastructure operational view? Is it possible to apply our lovely cloud native principals as well? What benefit’s both technologies could bring to each other?
Let me take this questions and provide you a short journey through existing deployment models and use cases for AI software. On practical examples, we discuss what cloud/on-premise strategy we may need for applying it to our own infrastructure to get it to work from an enterprise perspective. I want to give an overview about infrastructure requirements and technologies, what could be beneficial or limiting your AI use cases in an enterprise environment. An interactive Demo will give you some insides, what approaches I got already working for real.
Search and Society: Reimagining Information Access for Radical FuturesBhaskar Mitra
The field of Information retrieval (IR) is currently undergoing a transformative shift, at least partly due to the emerging applications of generative AI to information access. In this talk, we will deliberate on the sociotechnical implications of generative AI for information access. We will argue that there is both a critical necessity and an exciting opportunity for the IR community to re-center our research agendas on societal needs while dismantling the artificial separation between the work on fairness, accountability, transparency, and ethics in IR and the rest of IR research. Instead of adopting a reactionary strategy of trying to mitigate potential social harms from emerging technologies, the community should aim to proactively set the research agenda for the kinds of systems we should build inspired by diverse explicitly stated sociotechnical imaginaries. The sociotechnical imaginaries that underpin the design and development of information access technologies needs to be explicitly articulated, and we need to develop theories of change in context of these diverse perspectives. Our guiding future imaginaries must be informed by other academic fields, such as democratic theory and critical theory, and should be co-developed with social science scholars, legal scholars, civil rights and social justice activists, and artists, among others.
Let's dive deeper into the world of ODC! Ricardo Alves (OutSystems) will join us to tell all about the new Data Fabric. After that, Sezen de Bruijn (OutSystems) will get into the details on how to best design a sturdy architecture within ODC.
UiPath Test Automation using UiPath Test Suite series, part 3DianaGray10
Welcome to UiPath Test Automation using UiPath Test Suite series part 3. In this session, we will cover desktop automation along with UI automation.
Topics covered:
UI automation Introduction,
UI automation Sample
Desktop automation flow
Pradeep Chinnala, Senior Consultant Automation Developer @WonderBotz and UiPath MVP
Deepak Rai, Automation Practice Lead, Boundaryless Group and UiPath MVP
DevOps and Testing slides at DASA ConnectKari Kakkonen
My and Rik Marselis slides at 30.5.2024 DASA Connect conference. We discuss about what is testing, then what is agile testing and finally what is Testing in DevOps. Finally we had lovely workshop with the participants trying to find out different ways to think about quality and testing in different parts of the DevOps infinity loop.
Essentials of Automations: Optimizing FME Workflows with ParametersSafe Software
Are you looking to streamline your workflows and boost your projects’ efficiency? Do you find yourself searching for ways to add flexibility and control over your FME workflows? If so, you’re in the right place.
Join us for an insightful dive into the world of FME parameters, a critical element in optimizing workflow efficiency. This webinar marks the beginning of our three-part “Essentials of Automation” series. This first webinar is designed to equip you with the knowledge and skills to utilize parameters effectively: enhancing the flexibility, maintainability, and user control of your FME projects.
Here’s what you’ll gain:
- Essentials of FME Parameters: Understand the pivotal role of parameters, including Reader/Writer, Transformer, User, and FME Flow categories. Discover how they are the key to unlocking automation and optimization within your workflows.
- Practical Applications in FME Form: Delve into key user parameter types including choice, connections, and file URLs. Allow users to control how a workflow runs, making your workflows more reusable. Learn to import values and deliver the best user experience for your workflows while enhancing accuracy.
- Optimization Strategies in FME Flow: Explore the creation and strategic deployment of parameters in FME Flow, including the use of deployment and geometry parameters, to maximize workflow efficiency.
- Pro Tips for Success: Gain insights on parameterizing connections and leveraging new features like Conditional Visibility for clarity and simplicity.
We’ll wrap up with a glimpse into future webinars, followed by a Q&A session to address your specific questions surrounding this topic.
Don’t miss this opportunity to elevate your FME expertise and drive your projects to new heights of efficiency.
Transcript: Selling digital books in 2024: Insights from industry leaders - T...BookNet Canada
The publishing industry has been selling digital audiobooks and ebooks for over a decade and has found its groove. What’s changed? What has stayed the same? Where do we go from here? Join a group of leading sales peers from across the industry for a conversation about the lessons learned since the popularization of digital books, best practices, digital book supply chain management, and more.
Link to video recording: https://bnctechforum.ca/sessions/selling-digital-books-in-2024-insights-from-industry-leaders/
Presented by BookNet Canada on May 28, 2024, with support from the Department of Canadian Heritage.
When stars align: studies in data quality, knowledge graphs, and machine lear...
G1103034042
1. IOSR Journal of Electrical and Electronics Engineering (IOSR-JEEE)
e-ISSN: 2278-1676,p-ISSN: 2320-3331, Volume 11, Issue 3 Ver. III (May. – Jun. 2016), PP 40-42
www.iosrjournals.org
DOI: 10.9790/1676-1103034042 www.iosrjournals.org 40 | Page
Estimation of Hand Muscle Power
Prof. Abhinav V. Deshpande
Assistant Professor Department of Electronics & Telecommunication Engineering Prof. Ram Meghe Institute of
Technology & Research, Badnera, Amravati-444701
Abstract: In this research paper, the use of surface electromyogram is used to evaluate the hand muscle power
during various activities to grade the muscle strength. Our muscles have various rotations of movement and
actions, but with ageing, stroke, accident we lose some of its function. It can be a minor one like the action of
movement which are restricted when we do some physical actions. The losses can also be calculated from the
EMG of the muscle as for every motion the muscles produce an internal force. In this research work, analysis of
the function of upper limb is performed by recording the various EMG with different actions of the upper limb.
This proposed method will try to improve the existing method of muscle power grading and the muscle
functioning.
Keywords: Hand Muscle Power, Components, Electromyography, Signal Processing, Grading
I. Introduction
Electromyography (EMG) is a technique for evaluating and recording the electrical activity which is
produced by the skeletal muscles. The EMG is performed by using an instrument which is called as an
electromyography, to produce a record which is called as an electromyogram [1][2]. An electromyography
detects the electrical potential which is generated by the muscle cells when these cells are electrically or
neurologically activated. The signals can be analyzed to detect the medical abnormalities, activation levels and
recruitment order or to analyze the human or animal movement. The EMG potentials range in between 50 μV
and up to 20 to 30 mV depending on the muscles which are under observation [2][3]. In clinical practice, the
hand muscles are most often evaluated by using manual muscle strength testing by using the Medical Research
Council (MRC) Scale [2][4]. In this scale, the muscle strength is graded on a scale from 0 to 5. For evaluating
the strength of the intrinsic hand muscles, a small modification to the standard MRC grading has been made so
that grade 3 indicates “full active range of motion” as compared to “movement against gravity” [4][5].
Grade 5: Full active range of motion and normal muscle resistance
Grade 4: Full active range of motion and reduced muscle resistance
Grade 3: Full active range of motion and no muscle resistance
Grade 2: Reduced active range of motion and no muscle resistance
Grade 1: No active range of motion and Palpable muscle contraction only
Grade 0: No active range of motion and no palpable muscle contraction
Manual muscle testing however has a number of limitations. The limitation of this method is that the
scoring depends on the judgment of the examiner. Also with the 6-point ordinal MRC scale, it is difficult
manually to identify relatively small but clinically relevant changes in the muscle strength [5][6]. In order to
create more quantitative assessments of hand muscle strength, the dynamometers are more sensitive to the
change as compared to the manual muscle testing and render the outcome on a continuous scale [1][6]. In
clinical evaluation and research studies on patients with hand problems, muscle strength measurements are
usually based on the grip strength and pinch strength dynamometry. This research work aims to develop a
procedure which will aid in grading the muscle power in an automatic manner [5][6].
II. Research Methodology To Be Employed
The research work mainly consists of two parts such as EMG Acquisition and then analyzing the EMG signal.
2.1. EMG Acquisition
The acquisition of EMG can be done by using two basic types of electrodes viz. surface electrode and
needle electrodes. The former method is non-invasive and the latter method is invasive. But the level of
information which is obtained from the needle electrodes is high because the basic layers of skin are bypassed.
For primary screening, the surface electrodes are used for intense testing needle electrodes are used [1][5]. In
this research work, bipolar method of EMG acquisition is done. Two electrodes are placed on the biceps muscle
for EMG pick up while the third electrode acts as the ground to cancel the surface noise. The picked up signal is
passed to the EMG amplifier circuit which consists of instrumentation amplifier, high pass filter and low pass
filter [2][6]. The instrumentation amplifier amplifies the signal difference and rejects the input signals which are
2. Estimation of Hand Muscle Power
DOI: 10.9790/1676-1103034042 www.iosrjournals.org 41 | Page
common to both the input leads. The high pass filter has a cut-off frequency of 20 Hz while the low pass filter
has 3 KHz. The total gain of the EMG acquisition circuit is 1000. The circuit is interfaced with the computer by
using DAQ card [4][5].
2.2. Signal Analyzing
The acquired EMG signal is analyzed by using the MATLAB. For the analysis purpose, only one half
of the signal i. e. the signals which are lying above zero level is considered. So the signal is chopped from the
center to get only one half. Then the signal is converted to absolute form. During any action or contraction of
the muscles, the EMG signal produces a burst. The burst size, amplitude etc. varies according to the muscle
strength and the work is performed by the muscle. So this burst is separated from rest of the signal. Finally, the
burst analysis is done [2][4].
Figure 1 shows the processing of the EMG signal. Figure 1(a) shows the acquired signal, digitized
EMG signal, which is a bi-directional one. Figure 1(b) the rectified EMG signal. Whenever there is a muscle
contraction, EMG will appear as a burst. From the rectified signal, such EMG bursts alone are considered by
threshold method and windowing technique. The contraction burst is then used for analyzing the RMS, standard
deviation, maximum amplitude and the burst time [2][6].
Figure 1 Acquired EMG Signal
Figure 2 Positive Half of the Acquired EMG Signal
Figure 3 Burst of the Acquired EMG Signal
3. Estimation of Hand Muscle Power
DOI: 10.9790/1676-1103034042 www.iosrjournals.org 42 | Page
III. Results
In this research work, the EMG circuit has been designed with a gain of 1000 with a frequency of 20 to 3
KHz. The signals have been acquired from fifty persons with different actions. The people are in the age group
of 20 to 25 years. The RMS value, maximum amplitude and burst time directly gives the strength of the
muscles. The table shows the various analyzed values of a normal person [1][4].
Table 1 EMG Signal of a Normal Person
Performance Parameters Wrist Movement Normal Contraction Strong Contraction Contraction With Resistance
RMS 0.0125 0.0148 0.0165 0.0176
Maximum Amplitude 0.3872 0.9504 1.8692 1.9526
Burst Time 0.4500 1.0420 1.320 2.133
IV. Conclusion
The EMG circuit is designed and the signal is acquired. The amplitude analysis such as the RMS
contraction energy, maximum amplitude, standard deviation and one time analysis like the burst time for a
contraction will help in grading the muscle.
Acknowledgments
I am very much thankful to all of the staff members and the Head of Department, Electronics &
Telecommunication Engineering, Prof. Ram Meghe Institute of Technology & Research, Badnera, Amravati-
444701 for their kind support and co-operation in successful carrying out this research work. This research work
was undertaken as a part of Technical Education Quality Improvement Program (TEQIP-2) in order to promote
and facilitate the current and emerging trends in the field of Electronics & Telecommunication Engineering so
that the new and young researchers working in the fields of research and development in Electronics
Engineering domain should get the benefit of pursuing their main hobbies which are pertaining to the Embedded
Systems platform and should try to learn the new skills and expertise in the particular field of Embedded
Systems and Wireless Networks.
References
[1]. Anne F. Mannion, “The Use of Surface EMG Power Spectral Analysis in the Evaluation of Back Muscle Function”, Journal of
Rehabilitation Research and Development, Volume 34, No. 4, October 2007.
[2]. Carlo J. De Luca, “A Practicum Use of Surface EMG Signals in Movement Sciences”, 2002.
[3]. Kentaro Nagata, “Estimation of Muscle Strength During Motion Recognition Using Multi-Channel Surface EMG Signals”, IEEE
August 2008.
[4]. Qingling Li, Dongyan Wang, Zhijiang Du, Lining Sun, “A Novel Rehabilitation System for Upper Limbs”, Proceedings of the
September 2006 IEEE.
[5]. William Tam, Robert H. Allen, Yen Shi Gillian Hoe, Stanley Huang, “A Wireless Device for Measuring Hand-Applied Forces”,
Proceedings of the 26th
Annual International Conference of the IEEE EMBS September 2004.
[6]. Y. Y. Huang, K. H. Low and H. B. Lim, “Initial Analysis of EMG Signals of Hand Function Associated to Rehabilitation Tasks”,
Proceedings of the 2008 IEEE.